We implemented our method with Pytorch. For the FCN, the architecture is the same as in [1]}. We used the Adam optimizer with a learning rate of \(5e^{-4}\)  to train our model. For the hyper-parameters, we set \(\alpha _{1}=5\) , \(\alpha _{2}=1\) , \(\beta =0.5\) , \(\gamma =-0.5\) , \(\lambda =10\) , via grid search. All models were trained on an NVIDIA Quadro RTX 8000 GPU. The models with the lowest loss on the validation set were selected for evaluation.
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